Improving conversion rates through marketing analytics

Conversion rate optimization can heavily impact the bottom line, making it an important metric to track, especially for marketers. To meet business and consumer demands, marketers must constantly be optimizing their campaigns to improve conversion rates, thus maximizing ROI (return on investment) and maintaining relevance among consumers.

However, improving conversion rates is easier said than done. Marketers do not always have the right tools in place to make these high-value campaign updates, or if they do, they may not know where to focus their efforts. The most effective method for conversion rate optimization is through leveraging analytics. Analyzing the data gathered during campaigns allows marketing teams to determine what was most successful in driving conversions so they can then replicate that success moving forward. In this article, we examine how marketers can use analytics to improve conversion rates while avoiding common pitfalls. 

Setting a Conversion Rate Goal

Since businesses have different goals and different audiences, setting conversion rate goals can be challenging. The variables involved in determining conversion rates might change based on the type of campaign that is being run and the goal of that campaign. For example, for some, a conversion might mean a sale; for others it could mean having a form filled out, viewing a webinar, subscribing to a blog or a host of other actions. Additionally, depending on the channel the campaign is run on, conversion rates may be derived from the number of visitors to a website, viewers of a TV ad, etc. Ultimately, conversion rate is calculated the same way regardless of what is being measured: the number of conversions divided by the total number of visitors/viewers.

The goal of conversion rate optimization is to continuously optimize efforts to more effectively reach consumers. The first step is to set goals based on comparable benchmarks. These goals should be realistic based on the context of your industry. For example: A B2B (business-to-business) corporation that sells an advanced solution would not have the same conversion rate as a B2C (business-to-consumer) company that sells cheap electronics. The B2B organization has a more expensive product, meaning they will have to nurture leads longer before a final sale. They may count a conversion as a sales qualified lead, whereas the inexpensive products, which require a minimal investment, make the sales process faster and conversion rates higher.

With this in mind, the B2B company does not want to set a 40 percent conversion rate goal based on a standard set by the B2C company, because they have different products with different price points. Rather, it will need to identify a goal suited to its own industry. Some of the factors that can affect conversion rate include:

• Price: What is the price of the product? Products that have lower prices will have a faster sales cycle and will generally have higher conversion rates.
• Length of buyer’s journey: Is it a high investment product that will require more time and research before purchase, or something a consumer is likely to act upon quickly?
• Channel: What are the best places to reach your customers throughout the sales cycle? Are you promoting messaging in those locations?
• Traffic: How many people are seeing and engaging with your marketing material?
• Competition: How competitive is your space? Where does your product or brand rank among the competition in terms of awareness, price, reach, etc. 

Conversion Rate Optimization

Today’s marketing teams must constantly update their performance campaigns – campaigns that seek to elicit an action rather than build awareness – to build marketing plans that convert. These regular optimizations are necessary for myriad reasons.

First, today’s stakeholders and C-suites demand to see how every investment, marketing or otherwise, drives returns back to the business. As Ewan McIntyre, research director, Gartner for Marketers, notes, “As CMOs survey the landscape, one thing is clear – previous budget increases have come with weighty expectations, some of which have yet to be met.” If ROI is not apparent, budget to that campaign channel might be cut. This can be detrimental to marketing efforts, as perhaps the channel was reaching the right audience but with the wrong message.

If budget to market on that channel is cut before the message can be optimized, marketers are missing a chance to reach target buyers. Conversely, if marketers do not have the right tools to measure their advertising dollars, especially when it comes to brand awareness campaigns, they may be cutting channels that are driving revenue without realizing it.

Additionally, consumer demands are constantly shifting, and conversion rates can give marketers a clear indication of the direction in which they are evolving. Today’s buyers expect messages that are customized to their specific interests and are informed by their past purchases and engagements with the brand, with 80 percent of consumers noting they are more likely to do business with an organization that offers personalization. Having the ability to test out different messages and different levels of personalization across channels can help marketers build out more successful campaigns. Consider the “Share a Coke” campaign. By personalizing their bottles with user names and helping consumers find their own name in stores, Coca-Cola was able to achieve a 7 percent increase in adult consumption. Alternatively, Spartan Races targets users based on location when sending out emails. This led to a 13 percent increase in conversions. Marketing efforts that don’t cater to these new standards are more likely to be ignored or leave a bad taste in the buyer’s mouth, reducing conversions.

Well-optimized campaigns mean greater overall success. Messaging and placement will demonstrate a better understanding of core consumers that can be leveraged across departments, building a loyal customer base, generating more leads and producing greater profit. 

Factors that Inhibit Conversion Rates

Many marketers struggle with optimizing conversion rates in a way that hinders ROI or developing messaging that resonates with consumers. Two key challenges that come to mind when considering factors that inhibit conversion rate improvements, each of which surround data. First, marketers simply may not have access to the necessary data from which to derive optimization insights, or they may be relying on the wrong insights and incorrectly optimizing their campaigns. 

Lack Comprehensive Consumer Data

Marketing teams need to consider several personalized components when planning a high-converting campaign and overlooking some can result in drastically reduced reach and ROI.

Many marketers do not have the tools in place to understand consumer needs at the person level. These person-level insights are crucial to increased conversions, as they allow marketers to identify target audiences and create highly customized messaging that cuts through the noise to engage with them. Many personalized components need to be considered when planning a high-converting campaign, and overlooking one of them can result in drastically reduced reach and ROI. For example, maybe the audience you are targeting is too broad, you are choosing the wrong channel on which to display your message, or you have chosen the wrong creative or copy to resonate with your target consumers. Any one of these factors can cause a consumer to scroll by your message without a second thought.

Consider the Old Spice commercial titled “This is What your Man can Smell Like.” Old Spice had been considered an out-of-date brand and was trying to target younger users. After doing more extensive research, women were purchasing more than 50 percent of body washes. This ad sought to target not only men, but also women. By gaining more insight into who the buyers were, Old Spice was able to tailor its messaging to its audience and become a cultural phenomenon.

Additionally, many marketers lack visibility into the buyer’s journey. Effective optimization requires analytics that can demonstrate the exact path to conversion that a consumer took – noting which touchpoints they interacted with, in what sequence, and which played a greater role in driving them to the desired action. Without data that provides visibility into these paths, marketers will be challenged to draw accurate conclusions on which to improve conversion rates.

Relying on the Wrong Metrics

On the other hand, marketers may have analytics tools in place and visibility into the buyer’s journey but are relying on the wrong metrics.  

A common way marketers fall into this trap is by using an attribution model that does not provide a full view of the story. For example, single-touch attribution models such as last touch give full credit for a conversion to the last touchpoint engaged with before the consumer acts. Let’s say that the conversion goal is to get subscriptions to your company’s blog. Perhaps a consumer reads several blog posts they saw shared on social media channels. They then receive a promotional email with a CTA encouraging them to subscribe to the blog, which they finally do. Last-touch attribution would give full credit for this conversion to the email. However, engagement on social channels is what primed the user to have interest in this content. Without this insight, marketers may shift to focus their efforts entirely on email campaigns, ignoring the social engagements that earned a conversion.

Another pitfall is relying on older, legacy attribution models. Many marketers continue to rely on high-level models such as media mix modeling. This can be useful to discern sales trends over several years, but it cannot give marketers the person-level, real-time data required for success today.

These models can cause marketers to optimize away from what consumers really want.

De-optimizing campaigns is a serious risk when relying on the wrong data or attribution model. When consumers fail to connect with a campaign, it can lead to bad press or wasted spending. Campaigns are successful because media planners use the data to book the right channels and creative teams work together with the data to discover what these consumers would respond to.

Aside from the wrong attribution model, marketers may be leveraging vanity metrics, which can be misleading. For example, high engagement on a social post may not be reflective of purchase or conversion intent, while click-through rate may misrepresent the impact of an advertisement on moving consumers down the funnel. 

Finding the Right Measurement Model

Based on the challenges just discussed, we know that marketers don’t just need analytics, they need the right analytics. The first step to enhanced conversion rates is to ensure that your marketing team is using the correct attribution model to get the data that provides the necessary information to compete in today’s highly competitive marketing landscape. To determine which data is the correct data, marketers must identify which components of a performance campaign make the greatest impact on the choice to convert. These include:

• Creative and message: Marketers need to know what creative messaging resonates best with their target audience to attract their attention and get them to act.
• Channel: Marketers must optimize campaigns to ensure they are running on the channels that their target audiences use, as well as the audiences’ device preference.  
• Time: For consumers to convert, marketing material must reach them at the opportune time, when they are likely to stop and engage.  
• History: In today’s consumer landscape, any campaign must be informed by past interactions with the brand or past purchases. 

From there, they can identify which models will give them insight into these metrics and audience preferences, and move forward with optimization.

No attribution model is perfect, and thus getting information on all of these campaign elements will require a combination of multiple models, from multi-touch attribution to media mix modeling. Unified marketing measurement (UMM) is an attribution methodology that leverages multiple attribution models to give the most holistic understanding of consumer preferences, conversion trends over time and campaign effectiveness. UMM does this by centralizing and collating real-time, person-level data as well as aggregate data collected over several years to illuminate specific consumer needs alongside historical shopping and success trends. 

Quality Data Integration and Analysis

Once marketers have found the right attribution model, there is one more step to take before leveraging this data to optimize conversion rates. All of the data collected through the unified measurement model must be correlated and analyzed. Digital campaigns will generate massive amounts of data, which is completely useless if it cannot be wrangled and distilled into digestible insights.

This is a major hurdle marketers face on the path to conversion optimization. Though they have huge quantities of data available, they have trouble making sense of it all. To overcome this challenge, marketers must focus on data integration, representativeness and analysis.

Data integration: Because marketers are pulling data from so many different models and sources (offline and online campaigns) it is not initially directly comparable. Comparing results from a brand-building television campaign to a performance-focused digital campaign, or data from aggregate and person-level models is like comparing apples to oranges. Furthermore, manually correlating all of this data would take an immense amount of time. For this reason, marketers must invest in an analytics platform that leverages unified measurement and has the processing power to normalize the data from various models in a timely way.

Data representativeness: Another challenge marketers can face is ensuring that the data they are using to make decisions is actually representative of their customer base. As consumer behavior evolves and data volume grows, suspicious and fraudulent activities are increasing. Marketers need to ensure they can decipher all of the information they collect to get to the truth of their business and the truth of the behavior of the consumers they are targeting. When collecting data and prioritizing what data to use to make decisions, consider this:

• Is the data representative of the truth about the business and the consumers the business aims to influence?
• Will the data reveal the customer journey and allow you to examine converting paths to extract insights about the role of media in that journey?

Data analysis: Once this data is correlated and evaluated for representativeness, marketing teams need experienced data science teams to analyze it and derive actionable insights. Quality analysis is the most important part of conversion optimization. With the right data and tools, data scientists can understand how to reach consumers at the right time, with the right message, on the right channel, and apply those insights to campaigns to increase conversions and ROI. 

Leverage Analytics for Higher Conversion

Once marketers have determined the right analytics models, correlated the data and conducted a thorough analysis to attain actionable insights, they are on track to optimize their campaigns to achieve a higher conversion rate. This is done by understanding the interests and preferences of specific consumer segments and serving them the right message.

Analytics allow marketers to understand the creative and content that resonates with a target audience. With person-level analytics, marketers can segment by interest, creating content that appeals to different users. Tools that include heat mapping analytics allow marketers to evaluate the interests of a specific segment and create messaging tailored to them or develop messaging first before determining the ideal consumer. Consulting analytics when building and finalizing messaging also ensures that marketers are not missing any opportunities to convert potential customers. For example, if a company were launching a campaign to target parents, heat mapping might reveal that none of the messaging for a campaign appeals to mothers, which is one of the target audience segments. With this insight, marketing teams can develop additional creative to ensure they will convert these consumers.

In addition to optimizing messaging for conversions, marketers can also use analytics to enhance sequencing and select the right channel. By tracking engagements for each consumer, marketers can understand how the order in which consumers are exposed to ads makes them more or less likely to continue on the path to purchase. Campaigns can then be corrected to ensure optimal message sequences throughout the buyer’s journey. Moreover, analytics offers insight into the specific types of ads and channels that consumers are more inclined to engage with. For example, placing a native ad on Instagram rather than a display ad on Facebook.

Aggregate analytics can provide insight into long-term shopping trends that will dictate how and when marketers spend their budget. The high-level trends can account for factors such as seasonality, economic climate and more. While media mix modeling should not be relied on as a sole metric, it provides this high-level snapshot of historical data when determining how to best optimize campaigns for conversion. A simple example is seasonality. In the cold New England climate, data would show that marketing campaigns for hot chocolate converted more in the winter than the summer or that snow pants were more popular to buy early in the winter season. For many businesses, seasonality or weather can play a big role, so it is important to consider this as a factor when looking at campaigns. 

One More Thing

Once all of this data has been applied to campaigns and conversion rates are improving, don’t stop there. Marketers should constantly be testing new iterations of optimizations based on data and feedback gathered throughout campaigns. Leverage A/B testing techniques to see if there are areas of successfully converting campaigns that can be further finessed for even greater improvement.

When Electronic Arts launched SimCity 5, they did A/B testing with a few landing pages to discover which would drive more sales. In the first version, they had a banner that invited users to pre-order the game and save $20. However, when they removed the banner from the page, it drove 43 percent more purchases, which seemed counterintuitive to conventional wisdom. By relying on the data and being willing to test different versions, Electronic Arts was able to increase its profits. 

Final Thoughts

Meeting stakeholder and consumer demands while achieving overall business success requires marketers to constantly test new messaging and campaigns to find the combination that generates the most leads or sales. If these efforts are not informed by analytics, they are simply blind guesses. However, successfully gathering and analyzing this data is a multistep process that must set goals, evaluate current metrics in use, select an effective attribution model, and integrate and analyze data.  

From there, marketers can harness the power of analytics through a combination of person-level and aggregate attribution models, and continuously optimize campaign placement and messaging to ensure top results.